Case Study: Forecasting Wind Energy

Introduction

Our Achievements:

• Developed a comprehensive AI-driven forecasting tool specifically for wind energy.
• Improved forecast accuracy, reducing the error margin for neural network models by up to 6%.
• Automated manual data processing, significantly enhancing operational efficiency.
• Ensured regulatory compliance with evolving EU regulations.

Client Benefits:

• Provided precise metrics on wind energy production and forecasting accuracy.
• Enhanced operational efficiency by automating data input and processing.
• Ensured compliance with EU energy regulations with minimal manual intervention.

The Problems:

• Manual Data Processing: Their existing systems relied heavily on manual data input for wind energy, which was time-consuming and prone to errors.
• Regulatory Compliance: Keeping up with evolving EU regulations for energy production and reporting was complex and resource-intensive.
• Forecast Accuracy: Existing models did not provide the required precision for wind energy forecasting, leading to operational inefficiencies and potential financial losses.

Our Solution:

Path to Success:

• Data Verification and Analysis: The first step involved verifying and analyzing accessible data to ensure its accuracy and relevance, which was crucial for reliable forecasting and insights.
• Prototype Development and Integration: Pragmile developed and integrated a prototype of the advanced forecasting tool. This prototype utilized deep neural networks to enhance prediction accuracy, trained with weather and production data.
• Implementation and Feedback Collection: The tool was implemented and continuously refined based on feedback from Solar Spy, ensuring it met their specific needs.
• Advanced AI Model Training: The advanced AI models are currently being further trained and will be fed back into the system soon to accommodate upcoming 15-minute forecasting frequency requirements.